视频:一个全面的数据集,用于在各种环境中检测暴力
Abu Bakar Siddique Mahi1, Farhana Sultana Eshita1, Tabassum Chowdhury1
1Department of Computer Science and Engineering, University of Asia Pacific, Dhaka, Bangladesh.
Data in brief
|March 31, 2025
概括
一个由3020个视频片段组成的新数据集有助于自动化犯罪和暴力检测系统. 这种平衡的资源有助于训练和评估人工智能,以更快地识别事件,减少安全专业人员的手动审查.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 自动犯罪和暴力检测对于安全专业人员和执法人员来说至关重要,以减少手动视频分析.
- 现有的暴力检测数据集在范围,多样性和大小上往往是有限的,这阻碍了强大的AI系统的发展.
- 需要全面,平衡的数据集,反映现实世界的场景,以进行有效的培训和评估.
研究的目的:
- 引入一种新的,平衡的数据集,用于培训和评估用于犯罪和暴力侦测的自动化视频分析系统.
- 通过提供多样化和代表性的视频剪辑集合来解决现有数据集的局限性.
- 促进计算机视觉和机器学习模型在安全应用中的进步.
主要方法:
- 开发了一个包含3020个视频片段的数据集,均地分为暴力和非暴力行动.
- 包含了由非专业演员录制的从3秒到12秒的片段.
- 确保在数据集中捕捉到各种各样的现实世界情况.
主要成果:
- 数据集与1510个暴力和1510个非暴力片段相平衡,提供平等的代表性.
- 该系列以短视频片段捕捉的各种各样的现实场景为特色.
- 为提高自动检测系统的准确性和通用性提供了宝贵的资源.
结论:
- 开发的数据集比现有的自动暴力检测资源提供了显著的改进.
- 这种平衡和多样化的数据集将有助于开发更有效的AI安全工具.
- 该资源支持增强培训和评估,从而在现实世界的安全应用中提高性能.
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